BYOL[1]} is a self-supervised learning algorithm that learns image representations invariant to data augmentations.
Although contrastive learning methods such as SimCLR[2]} and MoCo[3]} also learn representations invariant to data augmentations as BYOL does, we believe that BYOL is appropriate for our purpose because it learns representations from a single input, whereas contrastive learning methods learn by comparison among input batch samples.
